Movement Quality and Mechanical Properties of the Muscle

Brief Summary

Movement quality is considered a comprehensive indicator of neuromuscular control, segmental stability, and efficient force transmission, and is recognized as an important determinant of musculoskeletal health and functional performance. Optimal movement patterns help distribute joint loading appropriately, whereas suboptimal movement strategies may contribute to increased biomechanical stress and the development of overuse injuries over time. In recent years, the passive mechanical properties of muscle (stiffness, tone, and elasticity) have been suggested to influence segmental stability and movement control; however, the relationship between these variables has not been sufficiently investigated in healthy populations. The aim of this study is to examine the association between movement quality and the passive mechanical properties of skeletal muscle in healthy adults. Healthy adult individuals will be included in the study. The sample size was determined based on a priori power analysis, and a total of 42 participants will be recruited. Movement quality will be assessed using the Functional Movement Screen (FMS). The FMS is a standardized screening tool consisting of seven fundamental movement tasks designed to evaluate basic movement patterns. Each task will be scored on a scale from 0 to 3, and the total score (0-21 points) will be used as an indicator of overall movement quality. The passive mechanical properties of muscle (stiffness, tone, and elasticity) will be measured using a portable and non-invasive device, the MyotonPRO 5, from the quadriceps, hamstring, gastrocnemius, tibialis anterior, and erector spinae muscles. Measurements will be performed at standardized anatomical reference points with the muscle in a relaxed position. The device probe will be placed perpendicular to the skin surface over the muscle, and the passive viscoelastic response of the tissue will be recorded. The relationship between variables will be analyzed using Pearson or Spearman correlation analysis, depending on data distribution characteristics.